Methods and apparatus to assign viewers to media meter data

ABSTRACT

Methods, apparatus, systems and articles of manufacture to assign viewers to media meter data are disclosed. An apparatus includes memory to store instructions, and a processor to execute the instructions to at least: determine first probabilities for first panelists in a media meter household during a first set of time periods, determine second probabilities for second panelists in a plurality of learning households during a second set of time periods, compare the first probabilities and the second probabilities to identify a candidate household from the plurality of learning households to associated with the media meter household, and impute ones of the first number of minutes to individual ones of the first panelists when second panelist behavior data associated with the candidate household indicates activity during one of the second set of time periods that matches activity in the media meter household during one of the first set of time periods.

RELATED APPLICATION

This patent arises from a continuation of U.S. patent application Ser.No. 14/866,158 (now U.S. Patent No. 10,219,039), entitled “Methods andApparatus to Assign Viewers to Media Meter Data” and filed on Sep. 25,2015, which claims the benefit of U.S. Patent Application Ser. No.62/130,286, entitled “Viewer Assignment of Household Viewers Without aPeople Meter” and filed on Mar. 9, 2015. Priority to U.S. patentapplication Ser. No. 14/866,158 and U.S. Patent Application Ser. No.62/130,286 is claimed. U.S. patent application Ser. No. 14/866,158 andU.S. Patent Application Ser. No. 62/130,286 are hereby incorporatedherein by reference in their respective entireties.

FIELD OF THE DISCLOSURE

This disclosure relates generally to market research, and, moreparticularly, to methods and apparatus to assign viewers to media meterdata.

BACKGROUND

In recent years, panelist research efforts included installing meteringhardware in qualified households that fit one or more demographics ofinterest. In some cases, the metering hardware is capable of determiningwhether a media presentation device (such as a television set) ispowered on and tuned to a particular station via a hardwired connectionfrom the media presentation device to the meter. In other cases, themetering hardware is capable of determining which household member isexposed to a particular portion of media via one or more button presseson a People Meter by the household member near the television.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example media distribution environment in whichhousehold viewers may be assigned to media meter data.

FIG. 2 is a schematic illustration of an example viewer assignmentengine constructed in accordance with the teachings of this disclosure.

FIG. 3 is a plot illustrating an example viewing index effect based onan age of collected data.

FIG. 4 is an example weighting allocation table to apply a temporalweight to collected minutes.

FIG. 5 is an example dimension table to illustrate cell dimensions andindependent probability dimensions.

FIGS. 6A-6C illustrate example combinations of dimensions.

FIGS. 7-10 illustrate example evaluations of persons of interest in amarket study.

FIG. 11 is an example most likely viewer table to associate averageprobability values and rank values for persons of interest.

FIG. 12 is an example most likely viewer matching table to identifyhousehold matches.

FIGS. 13 and 15 illustrate example household matching.

FIG. 14 is an example alignment table to align time periods betweentuning households and viewing households.

FIGS. 16-23 are flowcharts representative of example machine readableinstructions that may be executed to implement the example viewerassignment engine of FIGS. 1 and 2.

FIG. 24 is a schematic illustration of an example processor platformthat may execute the instructions of FIGS. 16-23 to implement theexample viewer assignment engine of FIGS. 1 and 2.

DETAILED DESCRIPTION

Market researchers seek to understand the audience composition and sizeof media, such as radio programming, television programming and/orInternet media so that advertising prices can be established that arecommensurate with audience exposure and demographic makeup (referred toherein collectively as “audience configuration”). As used herein,“media” refers to any sort of content and/or advertisement which ispresented or capable of being presented by an information presentationdevice, such as a television, radio, computer, smart phone or tablet. Todetermine aspects of audience configuration (e.g., which householdmember is currently watching a particular portion of media and thecorresponding demographics of that household member), the marketresearchers may perform audience measurement by enlisting any number ofconsumers as panelists. Panelists are audience members (householdmembers) enlisted to be monitored, who divulge and/or otherwise sharetheir media exposure habits and demographic data to facilitate a marketresearch study. An audience measurement entity typically monitors mediaexposure habits (e.g., viewing, listening, etc.) of the enlistedaudience members via audience measurement system(s), such as a meteringdevice and a People Meter. Audience measurement typically involvesdetermining the identity of the media being displayed on a mediapresentation device, such as a television.

Some audience measurement systems physically connect to the mediapresentation device, such as the television, to identify which channelis currently tuned by capturing a channel number, audio signaturesand/or codes identifying (directly or indirectly) the programming beingdisplayed. Physical connections between the media presentation deviceand the audience measurement system may be employed via an audio cablecoupling the output of the media presentation device to an audio inputof the audience measurement system. Additionally, audience measurementsystems prompt and/or accept audience member input to reveal whichhousehold member is currently exposed to the media presented by themedia presentation device.

As described above, audience measurement entities may employ theaudience measurement systems to include a device, such as the PeopleMeter (PM), having a set of inputs (e.g., input buttons) that are eachassigned to a corresponding member of a household. The PM is anelectronic device that is typically disposed in a media exposure (e.g.,viewing) area of a monitored household and is proximate to one or moreof the audience members. The PM captures information about the householdaudience by prompting the audience members to indicate that they arepresent in the media exposure area (e.g., a living room in which atelevision set is present) by, for example, pressing their assignedinput key on the PM. When a member of the household selects theircorresponding input, the PM identifies which household member ispresent, which includes other demographic information associated withthe household member, such as a name, a gender, an age, an incomecategory, etc. As such, any time/date information associated with themedia presented is deemed “viewing data” or “exposure data” (e.g.,“viewing minutes”) because it is uniquely associated with one of thehousehold panelist members. As used herein, “viewing data” isdistinguished from “tuning data” (e.g., “tuning minutes”) in which mediais presented within the household without a unique association with oneof the household panelist members. In the event a visitor is present inthe household, the PM includes at least one input (e.g., an inputbutton) for the visitor to select. When the visitor input button isselected, the PM prompts the visitor to enter an age and a gender (e.g.,via keyboard, via an interface on the PM, etc.).

The PM may be accompanied by a base metering device (e.g., a base meter)to measure one or more signals associated with the media presentationdevice. For example, the base meter may monitor a television set todetermine an operational status (e.g., whether the television is poweredon or powered off, a media device power sensor), and/or to identifymedia displayed and/or otherwise emitted by the media device (e.g.,identify a program being presented by a television set). The PM and thebase meter may be separate devices and/or may be integrated into asingle unit. The base meter may capture audience measurement data via acable as described above and/or wirelessly by monitoring audio and/orvideo output by the monitored media presentation device. Audiencemeasurement data captured by the base meter may include tuninginformation, signatures, codes (e.g., embedded into or otherwisebroadcast with broadcast media), and/or a number of and/oridentification of corresponding household members exposed to the mediaoutput by the media presentation device (e.g., the television).

Data collected by the PM and/or the base meter may be stored in a memoryand transmitted via one or more networks, such as the Internet, to adata store managed by a market research entity such as The NielsenCompany (US), LLC. Typically, such data is aggregated with datacollected from a large number of PMs and/or base meters monitoring alarge number of panelist households. Such collected and/or aggregateddata may be further processed to determine statistics associated withhousehold behavior in one or more geographic regions of interest.Household behavior statistics may include, but are not limited to, anumber of minutes a household media device was tuned to a particularstation (tuning minutes), a number of minutes a household media devicewas used (e.g., viewed) by a uniquely identified household panelistmember (viewing minutes) and/or one or more visitors, demographics of anaudience (which may be statistically projected based on the panelistdata) and instances when the media device is on or off. While examplesdescribed herein employ the term “minutes,” such as “tuning minutes,”“exposure minutes,” etc., any other time measurement of interest may beemployed without limitation.

To ensure audience measurement systems are properly installed inpanelist households, field service personnel have traditionally visitedeach panelist household, assessed the household media components,physically installed (e.g., connected) the PM and/or base meter tomonitor a media presentation device(s) of the household (e.g., atelevision), and trained the household members how to interact with thePM so that accurate audience information is captured. In the event oneor more aspects of the PM and/or base meter installation areinadvertently disrupted (e.g., an audio cable connection from the mediadevice to the base meter is disconnected), then subsequent field servicepersonnel visit(s) may be necessary. In an effort to allow collectedhousehold data to be used in a reliable manner (e.g., a mannerconforming to accepted statistical sample sizes), a relatively largenumber of PMs and/or base meters are needed. Each such PM and/or basemeter involves one or more installation efforts and installation costs.As such, efforts to increase statistical validity (e.g., by increasingpanel size and/or diversity) for a population of interest result in acorresponding increase in money spent to implement panelist householdswith PMs and/or base meters.

In an effort to increase a sample size of household behavior data and/orreduce a cost associated with configuring panelist households with PMsand/or base meters, example methods, apparatus, systems and/or articlesof manufacture disclosed herein employ a media meter (MM) to collecthousehold panelist behavior data. Example MMs disclosed herein aredistinguished from traditional PMs and/or base meters that include aphysical input to be selected by a panelist household member activelyconsuming the media. In examples disclosed herein, the MM captures audiowith or without a physical connection to the media device. In someexamples, the MM without the physical connection to the media deviceincludes one or more microphones to capture ambient audio in a roomshared by the media device. In some such examples, the MM captures codesembedded by one or more entities (e.g., final distributor audio codes(FDAC)), and does not include one or more inputs that are to be selectedby one or more household panelists to identify which panelist iscurrently viewing the media device. Rather than collecting audiencecomposition data directly from panelists, example methods, apparatus,systems and/or articles of manufacture disclosed herein impute whichhousehold members are viewers of media programming in households withthe MM. In other words, examples disclosed herein facilitate a manner ofdetermining which panelist household members are viewing media in amanner that avoids the expense of additional PM device installation inpanelist households.

Turning to FIG. 1, an example media distribution environment 100includes a network 102 (e.g., the Internet) communicatively connected tolearning households 104 and media meter (MM) households 106 within aregion of interest 108 (e.g., a target research geography). While theillustrated example of FIG. 1 includes a single region of interest 108,examples disclosed herein are not limited thereto, as any number ofadditional and/or alternate region(s) of interest may be considered. Inthe illustrated example of FIG. 1, the learning households 104 includePeople Meters (PMs) to capture media exposure information and identify acorresponding panelist household member(s) consuming the media, and theMM households 106 include media meters to capture media exposureinformation without identification of which household panelist member(s)is/are responsible for consuming the media. Behavior informationcollected by the example learning households 104 and the example MMhouseholds 106 are sent via the example network 102 to an example viewerassignment engine 110 for analysis. As described above, because MMhouseholds 106 do not include PMs, they do not include physical buttoninputs to be selected by household members (and/or visitors) to identifywhich household member is currently watching particular media.Additionally, such MM households 106 do not include physical buttoninputs to be selected by household visitors to identify age and/orgender information. Accordingly, examples disclosed herein reduceerrors, reduce data fluctuations, and improve stability of predictionsof which household members in the example MM households 106 are deemedto be viewers of (exposed to) media (e.g., viewers of media during aparticular daypart).

Example households that include a PM (i.e., the learning households 104)collect panelist audience data. As used herein, “PM panelist audiencedata,” “learning minutes” or “PM panelists” includes both (a) mediaidentification data (e.g., code(s) embedded in or otherwise transmittedwith media, signatures, channel tuning data, etc.) and (b) personinformation identifying the corresponding household member(s) and/orvisitor(s) that are currently watching/viewing/listening to and/orotherwise accessing the identified media. On the other hand, MMhouseholds 106 include only a MM to collect media data. As used herein,“media data,” “MM household minutes” and/or “media identifierinformation” are used interchangeably and refer to informationassociated with media identification (e.g., codes, signatures, etc.),but does not include person information identifying which householdmember(s) and/or visitors are currently watching/viewing/listening toand/or otherwise accessing the identified media. However, both theexample learning households 104 and the example MM households 106include panelists, which are demographically identified members of theirrespective households. As described above, at least one distinguishingfactor between PM panelists and MM panelists is that the former alsoincludes information that identifies which particular household memberis responsible for consuming media.

Although examples disclosed herein refer to code readers and collectingcodes, techniques disclosed herein could also be applied to systems thatcollect signatures and/or channel tuning data to identify media. Audiowatermarking is a technique used to identify media such as televisionbroadcasts, radio broadcasts, advertisements (television and/or radio),downloaded media, streaming media, prepackaged media, etc. Existingaudio watermarking techniques identify media by embedding one or moreaudio codes (e.g., one or more watermarks), such as media identifyinginformation and/or an identifier that may be mapped to media identifyinginformation, into an audio and/or video component. In some examples, theaudio or video component is selected to have a signal characteristicsufficient to hide the watermark. As used herein, the terms “code” or“watermark” are used interchangeably and are defined to mean anyidentification information (e.g., an identifier) that may be transmittedwith, inserted in, or embedded in the audio or video of media (e.g., aprogram or advertisement) for the purpose of identifying the media orfor another purpose such as tuning (e.g., a packet identifying header).As used herein “media” refers to audio and/or visual (still or moving)content and/or advertisements. To identify watermarked media, thewatermark(s) are extracted and used to access a table of referencewatermarks that are mapped to media identifying information.

Unlike media monitoring techniques based on codes and/or watermarksincluded with and/or embedded in the monitored media, fingerprint orsignature-based media monitoring techniques generally use one or moreinherent characteristics of the monitored media during a monitoring timeinterval to generate a substantially unique proxy for the media. Such aproxy is referred to as a signature or fingerprint, and can take anyform (e.g., a series of digital values, a waveform, etc.) representativeof any aspect(s) of the media signal(s) (e.g., the audio and/or videosignals forming the media presentation being monitored). A goodsignature is one that is repeatable when processing the same mediapresentation, but that is unique relative to other (e.g., different)presentations of other (e.g., different) media. Accordingly, the term“fingerprint” and “signature” are used interchangeably herein and aredefined herein to mean a proxy for identifying media that is generatedfrom one or more inherent characteristics of the media.

Signature-based media monitoring generally involves determining (e.g.,generating and/or collecting) signature(s) representative of a mediasignal (e.g., an audio signal and/or a video signal) output by amonitored media device and comparing the monitored signature(s) to oneor more references signatures corresponding to known (e.g., reference)media sources. Various comparison criteria, such as a cross-correlationvalue, a Hamming distance, etc., can be evaluated to determine whether amonitored signature matches a particular reference signature. When amatch between the monitored signature and one of the referencesignatures is found, the monitored media can be identified ascorresponding to the particular reference media represented by thereference signature that with matched the monitored signature. Becauseattributes, such as an identifier of the media, a presentation time, abroadcast channel, etc., are collected for the reference signature,these attributes may then be associated with the monitored media whosemonitored signature matched the reference signature. Example systems foridentifying media based on codes and/or signatures are long known andwere first disclosed in Thomas, U.S. Pat. No. 5,481,294, which is herebyincorporated by reference in its entirety.

In still other examples, techniques disclosed herein could also beapplied to systems that collect and/or otherwise acquire online data.Online data may include, but is not limited to online tags having astring of letters and/or numbers that are associated with media contentso that the media content can be identified. In some examples, the tagincludes attribute data and/or identifying information that has beenextracted from the media content. Example tag(s) can be associated withmedia content prior to distribution (e.g., before Internet media contentis streamed to presentation locations (e.g., households)). For example,the tag(s) may be associated with the media content in a webpagedistributing the media content, inserted in metadata of the mediacontent (e.g., in a file containing the media content or a fileassociated with the file containing the media content), inserted inmetadata of a stream, etc. The example tag(s) can later be extracted atpresentation location(s) and analyzed to identify the media content andincrement records for exposure to the media content.

FIG. 2 is a schematic illustration of an example implementation of theviewer assignment engine 110 of FIG. 1. In the illustrated example ofFIG. 2, the viewer assignment engine 110 includes a classificationengine 200, a probability engine 220, and a most likely viewer (MLV)engine 240. The example classification engine 200 of FIG. 2 includes anexample learning household interface 202, an example MM interface 204,an example weighting engine 206, an example cell generator 208, anexample stage selector 210, and an example independent distributionprobability (IDP) selector 212. The example probability engine 220 ofFIG. 2 includes an example total probability calculator 222, an examplemarginal probability calculator 224, an example odds ratio calculator226, and an odds appending engine 228. The example MLV engine 240 ofFIG. 2 includes an example cell selector 242, an example minutesaggregator 244, an example average probability calculator 246, and anexample rank engine 248.

In operation, the example viewer assignment engine 110 identifiescorresponding household members within the example MM households 106that are most likely viewers of media via three phases. In a firstphase, the example classification engine 200 classifies data from theexample learning households 104 and the example MM households 106 intomodel dimensions. In a second phase, the example probability engine 220identifies viewing probabilities for the example learning households 104with the aid of IDP dimensions. In a third phase, the example MLV engine240 uses those viewing probabilities to identify which example MMhouseholds 106 best match with corresponding example learning households104, and imputes the viewing behaviors of the matched example learninghousehold(s) 104 to the corresponding members of the example MMhousehold(s) 106.

First Phase—Classifying Data

In the example first phase, the example learning household interface 202acquires panelist (e.g., PM panelists) exposure minutes associated withlearning households 104 within a geography of interest (e.g., adesignated market area (DMA)), such as the example region of interest108 of FIG. 1. In some examples, data collected from households isassociated with a particular geographic area of focus, such asnationwide (sometimes referred to as a “National People Meter” (NPM)),while in other examples, household data is associated with a subset of aparticular geographic area of focus, such as a localized geography ofinterest (e.g., a city within a nation (e.g., Chicago), and sometimesreferred to as “Local People Meter” (LPM)).

As used herein, “exposure minutes” (also known as “viewing minutes”)refer to media data captured by a meter (e.g., a People Meter, a basemeter with panelist input identification capabilities, etc.) withinlearning households 104, in which the identified media is uniquelyassociated with a particular panelist member of the household (e.g., viaa People Meter button press). As used herein, “tuning minutes”distinguishes from exposure minutes and/or viewing minutes in that theformer refers to media data captured by a meter within MM households106, in which the identified media is not associated with a particularhousehold member. The example MM interface 204 acquires panelist tuningminutes associated with MM households 106 within the geography ofinterest.

When collecting behavior data from households, different degrees ofaccuracy result based on the age of the collected data. On a relativescale, when dealing with, for example, television exposure, an exposureindex may be computed. The example exposure index provides an indicationof how well PM data imputes exposure minutes, and may be calculated in amanner consistent with Equation (1).

$\begin{matrix}{{{Exposure}\mspace{14mu}{Index}} = \frac{{{No}.\mspace{14mu}{of}}\mspace{14mu}{imputed}\mspace{14mu}{PM}\mspace{14mu}{exposure}\mspace{14mu}{minutes}}{{{No}.\mspace{14mu}{of}}\mspace{14mu}{actual}\mspace{14mu}{exposure}\mspace{14mu}{minutes}}} & {{Equation}\mspace{14mu}(1)}\end{matrix}$

In the illustrated example of Equation (1), the exposure index iscalculated as the ratio of the number of imputed PM exposure minutes(e.g., “viewing minutes”) and the number of actual PM exposure minutes.While the example described above refers to minutes obtained fromlearning households 104, similar expectations of accuracy occur withdata (minutes) obtained from MM households 106.

The example exposure index of Equation (1) may be calculated on amanual, automatic, periodic, aperiodic and/or scheduled basis toempirically validate the success and/or accuracy of viewing behaviorimputation efforts disclosed herein. Index values closer to one (1) areindicative of a greater degree of accuracy when compared to index valuesthat deviate from one (1). Depending on the type of category associatedwith the collected exposure minutes, corresponding exposure index valuesmay be affected to a greater or lesser degree based on the age of thecollected data. FIG. 3 is an example plot 300 of exposure index valuesby daypart. In the illustrated example of FIG. 3, the plot 300 includesan x-axis of daypart values 302 and a y-axis of corresponding exposureindex values 304. Index value data points labeled “1-week” appear togenerally reside closer to index values of 1.00, while index value datapoints labeled “3-weeks” appear to generally reside further away fromindex values of 1.00. In other words, panelist audience data that hasbeen collected more recently results in index values closer to 1.00 and,thus, reflects an accuracy better than panelist audience data that hasbeen collected from longer than 1-week ago.

As described above, collected data that is more recent exhibits anaccuracy that is better than an accuracy that can be achieved withrelatively older collected data. Nonetheless, some data that isrelatively older will still be useful, but such older data is weightedless than data that is more recent to reflect its lower accuracy. Theexample weighting engine 206 applies a temporal weight, and appliescorresponding weight values by a number of days since the date ofcollection. Relatively greater weight values are applied to data that isrelatively more recently collected. In some examples, weight valuesapplied to collected tuning minutes and collected exposure minutes arebased on a proportion of a timestamp associated therewith. For instance,a proportionally lower weight may be applied to a portion of collectedminutes (e.g., tuning minutes, exposure minutes) when an associatedtimestamp is relatively older than a more recently collection portion ofminutes.

FIG. 4 illustrates an example weighting allocation table 400 generatedand/or otherwise configured by the example weighting engine 206. In theillustrated example of FIG. 4, exposure minutes were acquired from alearning household 104 (i.e., individualized panelist audience data) viaa PM device (row “A”), and household tuning minutes (i.e., minutes tunedin a household without individualizing to a specific person within thathousehold) were acquired from a MM household 106 via a MM device (row“B”). The example individualized panelist audience and household tuningminutes are collected over a seven (7) day period. In that way, the mostrecent day (current day 402) is associated with a weight greater thanany individualized panelist audience and/or household tuning minutesfrom prior day(s). The example individualized panelist minutes of row“A” may be further segmented in view of a desired category combinationfor a given household. Categories that characterize a household mayinclude a particular age/gender, size of household, viewed station,daypart, number of televisions, life stage, education level and/or otherdemographic attribute(s). For purposes of illustration, examplesdescribed below include the household age/gender category for thehousehold being male, age 35-54, the tuned station is associated with“WAAA” during the daypart associated with Monday through Friday between7:00 PM and 8:00 PM.

In the illustrated example of FIG. 4, the weighting engine 206 applies aweight value of 0.0017 to the first six (6) days of individualizedpanelist minutes and household tuning minutes, and applies a weightvalue of 0.99 to the most current day. While a value of 0.99 isdisclosed above, like the other values used herein, such value is usedfor example purposes and is not a limitation. In operation, the exampleweighting engine 206 of FIG. 2 may employ any weighting value in whichthe most current day value is relatively greater than values for one ormore days older than the current day. In connection with example datashown in the illustrated example of FIG. 4 (e.g., days one through sixhaving 34, 17, 26, 0, 0 and 20 exposure minutes, respectively, thecurrent day having 37 exposure minutes, days one through six having 40,30, 50, 0, 0 and 30 household tuning minutes and the current day having50 household tuning minutes), a weighted exposure minutes value yields36.79 and a weighted household tuning minutes value yields 49.75. Insome examples, the probability engine 220 calculates an imputationprobability that a MM panelist (e.g., a panelist household with only aMM device and no associated PM device) with the aforementioned categorycombination of interest (e.g., male, age 35-54 tuned to channel WAAAduring Monday through Friday between the daypart of 7:00 PM and 8:00 PM)is actually viewing this tuning session. The probability is calculatedby the example probability engine 220 by dividing the weighted exposureminutes (e.g., 36.79 minutes) by the weighted household tuning minutes(e.g., 49.75 minutes) to yield a 74% chance that the MM panelist withthis same household category combination is associated with this tuningbehavior. While examples disclosed herein refer to probabilitycalculations, in some examples odds may be calculated to bound resultsbetween values of zero and one. For example, odds may be calculated as aratio of a probability value divided by (1−Probability). If desired, theodds may be converted back to a probability representation.

Categories (sometimes referred to herein as “dimensions”) within theexample learning households 104 and the example MM households 106 may bedifferent. A market researcher may have a particular dimensioncombination of interest when attempting to determine which householdmembers of an example MM household 106 were actually consuming media(e.g., a household having males, age 35-54, etc.). When attempting tomatch one or more MM households 106 with one or more learning households104, examples disclosed herein identify candidate households that havean appropriate (similar) match of dimensions. Sets of dimensions arecategorized by the example cell generator 208, in which different setsrepresent requirements for particular ones of the learning households104 and particular ones of the MM households 106, as described infurther detail below.

FIG. 5 illustrates an example dimension table 500 that identifiescombinations of dimensions required for households (both learninghouseholds 104 and MM households 106) when computing probabilities.Different cell combinations may be required based on a household size ofone 502, or a household size of two or more 504. Additionally, theexample dimension table 500 describes dimension combinations at a celllevel, which reflect a requirement that a particular household includesa combination of all listed dimensions within the cell. When anoccurrence of all listed dimensions of a cell are present within ahousehold, those dimensions are deemed to be “intersecting.” Forinstance, if a candidate learning household includes each of the exampledimensions in a first stage (Stage 1) 506, then that particularhousehold is to be matched only with other learning households and MMhouseholds that also represent (intersect) all of those dimensions. Inthe illustrated example of FIG. 5, the first stage cell for a householdof size 2+ includes the dimensions of age/gender, household size 2+, aroom location type, a number of kids value, a number of adults 2+, anaffiliate/genre type, a person type, a daypart and a number of sets(televisions).

Generally speaking, a number of households in a research geography ofinterest matching a single one of the dimensions of interest may berelatively high. However, as additional dimensional requirements areadded for the study, the number of qualifying households having aninclusive match for all such dimensions decreases. In somecircumstances, the number of matching households in a donor pool afterperforming a logical “AND” of all dimensions of interest eventuallyresults in that donor pool having a population lower than a thresholdvalue, which may not exhibit statistical confidence when applyingprobability techniques to determine which household members are actuallyviewing within the MM homes. In the event a particular cell does notcontain enough households to satisfy the dimension requirements of theStage 1 cell 506, a Stage 2 cell 508 is considered, which includes arelatively lower number of required dimensions to intersect.Additionally, the example dimension table 500 includes a Stage 3 cell510 in the event a particular cell does not include the complete numberof dimensional requirements of the Stage 2 cell 508. While theillustrated example of FIG. 5 includes three example stages, examplesdisclosed herein are not limited thereto.

As described above, dimensions within a cell reflect a logical “AND”condition of representation (e.g., they are intersecting dimensions).However, examples disclosed herein also consider dimensionsindependently in an effort to reduce imputation errors, reduce datafluctuations and improve data stability. Independent distributionprobability (IDP) dimensions are associated with each example stage.Generally speaking, IDP dimensions enable an improvement on thestatistical reliability when imputing potential viewing (tuning) in theMM households as actual viewing (exposure). The example IDP dimensionsimprove data granularity and predictive confidence of the imputation,and allows other dimensions deemed relevant to an analyst to beconsidered that might not otherwise be permitted (e.g., due to samplesize restrictions). In some examples, one or more IDP dimensions areempirically determined to be valuable to different demographiccharacteristics of the household under consideration for imputation. Inthe illustrated example of FIG. 5, the dimension table 500 includes aStage 1 IDP level 512, a Stage 2 IDP level 514 and a Stage 3 IDP level516. In the illustrated example of FIG. 5, the IDP dimensions include adaypart dimension, a station code dimension, and a Spanish dominantdimension. Examples disclosed herein are not limited thereto and mayinclude additional and/or alternate dimensions of interest such as anAsian dimension, an African American dimension, and/or a Blackdimension. As described in further detail below, example IDP dimensionsare used to generate probabilities from household tuning minutes andhousehold exposure minutes independently from the cell dimensions.Stated differently, while the illustrated example of FIG. 5 includesthree separate IDP dimensions of Stage 1 (512), those three IDPdimensions do not require a logical “AND” condition between during theanalysis. Instead, each one may be evaluated independently of the othersin view of the qualifying households associated with the Stage 1 celldimensions 512.

While the illustrated example of FIG. 5 lists example dimensions forcells and IDP levels, such examples are shown for purposes ofexplanation. Different combinations of dimensions are shown in theillustrated example of FIG. 6A for an example Affiliate/Genre dimension602, an example Broad Affiliate/Genre dimension 604, an examplehousehold under test (HUT) dimension 606, and an example Age/Genderdimension 608. The illustrated example of FIG. 6B includes an exampleDaypart (30-way) Weekday dimension 610, an example Daypart (30-way)Weekend dimension 612, and an example Daypart (5-way) dimension 614. Theillustrated example of FIG. 6C includes an example Household Sizedimension 616, an example Number of Adults dimension 618, an exampleNumber of Kids dimension 620, an example Person Type (3-way) dimension622, an example Room Location dimension 624, an example Number of Setsdimension 626, an example Person Type (Relative) dimension 628 and anexample Spanish Dominant dimension 630.

For a market study of interest, the market researcher may identify atarget set of dimensions of interest with which to determine viewingbehavior in MM households 106. For example, the market researcher mayseek to learn about households in a Pacific territory with a membershipsize of three having (a) one male age 35-54, (b) one female age 35-54and (c) one child age 2-11. In view of these desired dimensions ofinterest, examples disclosed herein identify matches of learninghouseholds 104 and MM households 106 (and their corresponding behaviordata). As described above, the first phase classifies household datainto model dimensions. While examples below refer to classifyinglearning households 104, such examples may also consider classificationfrom the example MM households 106.

The example cell generator 208 retrieves the target set of dimensions ofinterest for the study, and the example stage selector 210 selects aninitial candidate stage of intersecting dimensions, such as the exampleStage 1 cell dimensions 506. The example stage selector 210 determines anumber of households within the geography of interest that meet thedimensional requirements of the Stage 1 cell dimensions 506. For thesake of example, assume that sixty (60) households have at least three(3) household members, two (2) adults, one (1) child, are watching anews genre, a set in the living room, and during a daypart of Mondaythrough Sunday between 7:00 to 11:00 PM. In view of each of the personsof interest (e.g., demographic dimensions of interest for the study),such as (a) the example male age 35-54, (b) the example female age35-54, and (c) the example child age 2-11, the example stage selector210 identifies, out of the total number of sixty (60) households, howmany households containing each person are included. The example stageselector 210 compares the number of households with each person ofinterest to a threshold value to determine whether Stage 1 isappropriate. If so, then the person of interest is designated asassociated with Stage 1 dimensions for the remainder of the marketstudy, in which only data from Stage 1 qualifying households will beused for probability calculations.

However, in the event one or more households do not satisfy thethreshold, then the example stage selector 210 evaluates a subsequentstage (e.g., Stage 2 (508)) to determine whether a threshold number ofqualifying households is available. As described above, subsequent cellstages include a relatively lower number of intersecting dimensions,thereby increasing the possibility that a greater number of availablehouseholds will qualify (e.g., contain all of the dimensions). FIG. 7illustrates an example evaluation of each of the persons of interest. Inthe illustrated example of FIG. 7, the example stage selector 210determines a number of homes that include all of the cell dimensionsfrom Stage 1 (see reference 702). Therefore, the Stage 1 threshold issatisfied for both the adult male and the adult female in the examplehousehold. Because the example child age 2-11 is not represented in thepotential households from cell Stage 1, the example stage selector 210evaluates the child age 2-11 in view of a subsequent stage (i.e., Stage2) (see reference 704).

FIG. 8 illustrates an example evaluation of cell Stage 2 for the childage 2-11, in which Stage 2 utilizes less restrictive dimensionalrequirements than Stage 1. In the illustrated example of FIG. 8, theexample stage selector 210 determines how many homes match thedimensional requirements of cell Stage 2 including a child age 2-11 (see508 of FIG. 5). Because thirty-six (36) households include a child age2-11, and because that value satisfies a sample size threshold value,the example stage selector 210 classifies the corresponding person ofinterest (i.e., child age 2-11) to use only those households thatsatisfy Stage 2 dimensional requirements. As described above, theseexamples classify both learning data households 104 and MM datahouseholds 106, though each sample is considered separately for purposesof determining if the number of homes passes the threshold for use ofstage 1, 2 or 3.

While the above examples classify in view of the cell dimensions, whichrequire a logical “AND” condition to qualify, examples disclosed hereinalso classify IDP dimensions, which are evaluated independently withineach cell. FIG. 9 illustrates an example evaluation of IDP dimensionsassociated with Stage 1, in which a first one of the persons of interestis considered (i.e., the male age 35-54) (902). Because the male age35-54 was previously classified as belonging to Stage 1, correspondingIDP dimensions also associated with Stage 1 are evaluated to determinewhether a threshold number of households are representative. In theillustrated example of FIG. 9, three example IDP dimensions are shown904. Each of these example IDP dimensions of interest includes arepresentative number of qualifying households 906. For the sake ofexample, assume that a threshold value of thirty (30) households must berepresented to allow the corresponding IDP dimension to be used whencalculating probabilities. As such, all three of the example IDPdimensions associated with the male age 35-54 qualify, and will be used.While the illustrated example of FIG. 9 does not show an evaluation ofthe female age 35-54, the same process is used.

In the illustrated example of FIG. 10, the child age 2-11 is evaluatedin view of IDP dimensions corresponding to Stage 2 based on the factthat the child age 2-11 was previously classified using Stage 2dimensions. From the previously identified quantity of households fromStage 2 having a child age 2-11 (1008), the example IDP selector 212determines whether each example IDP dimension 1010 includes arepresentative number of qualifying households 1012. Again, for the sakeof example, assume that a threshold value of thirty (30) households mustbe represented to allow the corresponding IDP dimension to be used whencalculating probabilities for the child age 2-11. In this case, the IDPdimension “tuned during M-F 7-8 pm daypart” only included eighteen (18)qualifying households and the IDP dimension “are non-Hispanic” onlyincluded twenty-two (22) qualifying households. As such, neither ofthese two IDP dimensions qualify and will not be used when calculatingprobabilities, as discussed in further detail below. However, the IDPdimension “tuned to WAAA” included thirty-four (34) qualifyinghouseholds, which satisfies the example threshold of thirty (30). Assuch, this IDP dimension will be retained and/or otherwise used whencalculating probabilities.

Second Phase—Calculating Probabilities

Now that potential viewers are categorized into respective celldimensions (intersecting dimensions) and IDP dimensions based on samplesize thresholds of qualifying households (both learning households 104and MM households 106), the example first phase of classifyinghouseholds is complete. As used herein the term potential viewing refersto household tuning behaviors that have not been confirmed to beassociated with a specific household member. For instance, a MMhousehold may log and/or otherwise collect tuning behavior for aparticular quarter hour (QH), in which any one of the household memberstherein could potentially be responsible for consuming and/or otherwiseviewing the media during that particular QH. In some examples, the MMhouseholds are referred to herein as “tuning households” to reflect thatthe data collected therein includes, for example, an amount of time(e.g., minutes) of media detected in the household, but without acorresponding uniquely identified member within that household. In suchcircumstances, panelist members within the tuning household(s) may bereferred to as “tuning panelists.” Unless and until actual tuningbehavior can be confirmed and/or otherwise attributed to a specificperson or persons within the home, the household members during thatparticular QH are deemed potential viewers as distinguished from actualviewers.

Next, and as described above, the example probability engine 220identifies viewing probabilities for the example learning households 104with the aid of respective IDP dimensions associated with thequalification criteria. In some examples, the learning households arereferred to herein as “viewing households” to reflect that the datacollected therein includes, for example, an amount of time (e.g.,minutes) of media detected in that household, which includes uniqueidentification of which household member is exposed to and/or otherwiseconsuming that media. In such circumstances, panelist members within theviewing households may be referred to as “viewing panelists.” Inoperation, the example classification engine 200 selects a demographicof interest (person of interest) associated with one of the previouslyclassified stages (e.g., Stage 1, Stage 2, etc.). For example, in theevent males age 35-54 is selected as the demographic of interest, thenthe example learning household interface 202 retrieves and/or otherwisereceives corresponding exposure minutes (viewing minutes) from allhouseholds that match the classified cell dimensions (e.g., within Stage1 dimensions). Additionally, the example learning household interface202 retrieves and/or otherwise receives corresponding exposure minutesfrom those households that are associated with all other demographicmembers within those households, such as an associated female age 35-54and/or child age 2-11. Again, the exposure minutes retrieved areassociated with only those households that were previously identified tosatisfy a threshold representative number of households matching thesame stage cell dimensions (e.g., the cell dimensions of Stage 1, thecell dimensions of Stage 2, etc.).

The example total probability calculator 222 calculates a totalprobability in a manner consistent with example Equation (2)

$\begin{matrix}{{{{Tot}.\mspace{14mu}{Probability}}\mspace{14mu}(j)} = \frac{\sum{{Exposure}\mspace{14mu}{Minutes}\mspace{14mu}{for}\mspace{14mu} j}}{\sum{{Potential}\mspace{14mu}{Exposure}\mspace{14mu}{Minutes}\mspace{14mu}{for}\mspace{14mu} j}}} & {{Equation}\mspace{14mu}(2)}\end{matrix}$

In the illustrated example of Equation (2), j reflects one of thedimensions of interest under study, such as, in this example, a male age35-54. That particular male came from a household that satisfied thethreshold number of households that also contain Stage 1 cell dimensionsof three (3) household members, two (2) adults, one (1) child, viewing anews genre, a set in a living room, and viewing within the daypart ofMonday through Sunday between the hours of 7:00 PM through 11:00 PM. Inthis example, assume that the males age 35-54 are associated with 1850exposure minutes, in which that value is the sum for all householdssatisfying the Stage 1 cell dimensions. Also in this example, assumethat other household member persons of interest under analysis (e.g.,females age 35-54 and children age 2-11) account for 2250 exposureminutes within those respective households. Stated differently, minutesassociated with other household minutes are deemed “potential exposureminutes” because of the possibility that they could have also beenviewing at the same time as the male age 35-54.

Applying the example scenario above to example Equation (2) yields atotal probability for the male age 35-54 as 0.74. A total odds value maybe calculated in a manner consistent with example Equation (3).

$\begin{matrix}{{{{Tot}.\mspace{14mu}{Odds}}\mspace{14mu}(j)} = \frac{{Total}\mspace{14mu}{Probability}\mspace{14mu}(j)}{\left\lbrack {1 - {{Total}\mspace{14mu}{Probability}\mspace{14mu}(j)}} \right\rbrack}} & {{Equation}\mspace{14mu}(3)}\end{matrix}$

In the event probability values and total odds values are to bedetermined for one or more additional persons of interest within amarketing study, such as the example female age 35-54 and/or the examplechild age 2-11, then a similar approach is repeated using exampleEquations (2) and (3) with respective exposure minutes for those personsof interest.

As described above, in an effort to reduce imputation errors, examplesdisclosed herein also incorporate IDP dimensions associated with eachstage. In some examples, the IDP dimensions may reduce/resolve datafluctuations and/or improve data stability, thereby improvingcomputation efficiency by lowering one or more evaluation iterations.For each person of interest, a corresponding one or more IDP dimensionmarginal probabilities is calculated. Also as described above, somepersons of interest may have relatively greater or fewer IDP dimensionsto be calculated depending on whether that person of interest is alsoassociated with a threshold number of households that qualify.Continuing with the example person of interest male age 35-54, it waspreviously determined that IDP dimensions of (a) Monday through Friday7:00 PM to 8:00 PM daypart, (b) tuned to station WAAA and (c)Non-Hispanic each included at least thirty (30) qualifying householdswithin Stage 1. As such, the example marginal probabilities for each ofthese persons of interest is calculated based on exposure minutes fromthose households in which the cell dimensions were previouslyidentified. However, rather than require that each of the IDP dimensionsall simultaneously be present within those households, each one of theIDP dimensions is evaluated in an independent manner so that there isone IDP marginal probability calculated for each IDP dimension in amanner consistent with example Equation (4).

$\begin{matrix}{{{Marginal}\mspace{14mu}{Probability}\mspace{14mu}\left( {j,{di}} \right)} = \frac{\sum{{Exposure}\mspace{14mu}{Minutes}\mspace{14mu}{for}\mspace{14mu}\left( {j,{di}} \right)}}{\sum{{Potential}\mspace{14mu}{Exposure}\mspace{14mu}{Minutes}\mspace{14mu}{for}\mspace{14mu}\left( {j,{di}} \right)}}} & {{Equation}\mspace{14mu}(4)}\end{matrix}$In the illustrated example of Equation (4), j reflects one of thedimensions of interest under study, such as, in this example, a male age35-54. Additionally, di reflects an IDP dimension, such as (a) Mondaythrough Friday 7:00 PM to 8:00 PM daypart, (b) tuned to station WAAA or(c) Non-Hispanic. In this example, assume that for the person ofinterest males age 35-54, in which a total probability (andcorresponding total odds) was previously calculated, account for 600exposure minutes, and that the other household member persons ofinterest account for 850 exposure minutes. When applying exampleEquation (4), a marginal probability for males age 35-54 in connectionwith the IDP dimension Monday through Friday 7:00 PM to 8:00 PM daypartresults in a marginal probability of 0.71. Example Equation (4) may thenbe reapplied in view of one or more additional available IDP dimensionsto calculate additional marginal probability value(s).

Marginal odds associated with each marginal probability calculation maybe determined in a manner consistent with example Equation (5).

$\begin{matrix}{{{Marginal}\mspace{14mu}{Odds}\mspace{14mu}\left( {j,{di}} \right)} = \frac{{Marginal}\mspace{14mu}{Probability}\mspace{14mu}\left( {j,{di}} \right)}{\left\lbrack {1 - {{Marginal}\mspace{14mu}{Probability}\mspace{14mu}\left( {j,{di}} \right)}} \right\rbrack}} & {{Equation}\mspace{14mu}(5)}\end{matrix}$Additionally, for each IDP dimension, a corresponding odds ratio iscalculated in a manner consistent with example Equation (6).

$\begin{matrix}{{{Odds}\mspace{14mu}{Ratio}\mspace{14mu}\left( {j,{di}} \right)} = \frac{{Marginal}\mspace{14mu}{Odds}\mspace{14mu}\left( {j,{di}} \right)}{{Total}\mspace{14mu}{Odds}\mspace{14mu}(j)}} & {{Equation}\mspace{14mu}(6)}\end{matrix}$

When all persons of interest have been considered to calculaterespective (a) total probabilities (and associated total odds), (b)marginal probabilities (and associated marginal odds) and (c) oddsratios, examples disclosed herein apply and/or otherwise impute thosecalculated probabilities to households associated with the MM panelists(MM households 106). In particular, the example classification engine200 identifies MM households 106 that have dimensions that match theexample learning households 104. As described above, the examplelearning households 104 now have corresponding total probability values(associated with cell dimensions), total odds values (associated withcell dimensions), marginal probability values (associated with IDPdimensions) and marginal odds values (associated with cell and IDPdimensions). The aforementioned total probability values, total oddsvalues, marginal probability values and marginal odds values from theexample learning households 104 are imputed by the exampleclassification engine 200 to corresponding MM households 106 having thesame matching dimensions. For each demographic of interest, the exampleodds appending engine 228 calculates an adjusted odds value in a mannerconsistent with example Equation (7).Adjusted Odds(j, d)=Total Odds(j)×Odds Ratio (j, d1)×Odds Ratio (j, d2)×. . . Odds Ratio (j, dn)  Equation(7).In the illustrated example of Equation (7), j reflects a dimension ofinterest under analysis, such as a male age 35-54, and d_(n) reflects anIDP dimension of interest under analysis. Additionally, the exampleappending engine 228 calculates a final probability in a mannerconsistent with example Equation (8).

$\begin{matrix}{{{Final}\mspace{14mu}{Probability}} = \frac{{Adjusted}\mspace{14mu}{Odds}\mspace{14mu}\left( {j,d} \right)}{\left\lbrack {1 + {{Adjusted}\mspace{14mu}{Odds}\mspace{14mu}\left( {j,d} \right)}} \right\rbrack}} & {{Equation}\mspace{14mu}(8)}\end{matrix}$After the application of example Equations 2-8, final probability valuesare available for all observations in both the example learninghouseholds 104 and the example MM households 106, which ends the secondphase. However, probability calculations may be repeated in someexamples, such as when a station or station genre changes, when tuningcontinues to another daypart, cell classification changes, etc.

Third Phase—Most Likely Viewers

As described above, the example third phase uses the final probabilityvalues to identify best matches of each TV set within the MM households106 and learning households 104 so that the viewing behaviors on each TVset from the members of the learning households may be imputed to thecorresponding members of the matching MM households 106. In operation,the example MM interface 204 selects one of the MM households 106 andthe example classification engine 200 identifies a correspondingclassification that was previously associated with that selected MMhousehold. As described above, each person was classified as qualifyingfor a particular cell and stage, in which each stage includes aparticular combination of model dimensions. This phase of the examplemethodology utilizes a subset of these dimensions as must-match criteriabetween each MM household/TV set and learning household/TV set to ensurecharacteristic and behavioral similarity between the matched TV setswithin the homes. Additionally, for each TV set within each MMhousehold, this phase of the example methodology finds the best matchinglearning household/TV set to impute viewers. Further, this phase of theexample methodology is carried out by cell such that a best matchinglearning household/TV set is determined multiple times for any givenday. That is, if, for example, a station or station genre changes, orthe tuning continues to another daypart, or any other aspect of the datachanges such that it is classified into a different cell, then thematching process is carried out again. Therefore, each MM household/TVset can be matched with different learning households/TV sets throughoutthe day; the best matching, most similar is always selected.Additionally, and as described in further detail below, preferences maybe identified for homes within a particular DMA.

For each TV set, example cell, and person ID, the example averageprobability calculator 246 calculates an average probability value, asshown in FIG. 11. In the illustrated example of FIG. 11, a portion of amost likely viewer (MLV) table 1100 includes a household/set column 1102(similar to that shown in FIG. 10), a cell combination column 1104, aperson ID column 1106 (similar to that shown in FIG. 10), an averageprobability column 1108, and an MLV rank column 1110. The example cellcombination column 1104 illustrates a first combination. For each personwithin the selected MM household, the example average probabilitycalculator 246 calculates an average probability across all quarterhours within the cell (e.g., the average probability for all quarterhours per household person within the daypart between 7:00-11:00 PM, asdefined by the example dimension classification), as shown in theexample average probability column 1108. Based on the averageprobability values, the example rank engine 248 establishes an MLV rankfor each person, as shown in the example MLV rank column 1110. While theillustrated example of FIG. 11 includes two example cell combinationsfor the Smith household, any number of additional households, setswithin households (e.g., a living room set, a bedroom set, etc.), and/orcell combinations of interest (e.g., 4:00-7:00 PM daypart) may be addedto the example MLV table 1100. While examples disclosed above generateaverage persons probabilities and rankings associated with classified MMhouseholds 106, a similar generation of average persons probabilitiesand corresponding ranking also occurs in connection with the classifiedlearning households 104. As described in further detail below, theseaverage probabilities and corresponding rankings for each of thelearning households 104 and MM households 106 are compared to identify abest match.

The example MLV engine 240 next matches the ranked MM households tocorresponding learning households. In operation, the example MMinterface 204 selects one of the MM households 106 and the examplelearning household interface 202 selects one or more candidate learninghouseholds 104 for comparison purposes, as shown in FIG. 12. In theillustrated example of FIG. 12, an MLV matching table 1200 includes datacolumns associated with MM households 106 to be matched to learninghouseholds (1201), which includes a household/set column 1202 (similarto that shown in FIGS. 10 and 11), an MLV rank column 1204 (similar tothat shown in FIG. 11), and an average probability column 1206 (similarto that shown in FIG. 11). In the illustrated example of FIG. 12, theaverage probability column 1206 includes average tuning probabilityvalues because such data is associated with MM households (tuninghouseholds). Additionally, to identify which one of any number ofcandidate learning households best matches the candidate MM householdunder evaluation (e.g., in this example the “Smith household, Set 1”),the example MLV matching table 1200 includes data columns associatedwith candidate learning households 104 to be matched 1208. Inparticular, the example MLV matching table 1200 includes a household/setcolumn 1210, an MLV rank column 1212, and an average probability column1214. In the illustrated example of FIG. 12, the average probabilitycolumn 1214 includes average viewing probability values because suchdata is associated with learning households (viewing households).

The example MLV engine 240 calculates an absolute difference between theaverage probability values for each household person, which is shown inthe example absolute difference column 1216. Additionally, for eachcompared MM household and learning household, the example MLV engine 240calculates an MLV score based on the sum of the absolute differencevalues, which is shown in the example MLV score column 1218. Generallyspeaking, an MLV score value that is relatively lower compared to otherMLV score values indicates a greater degree of similarity between thecompared persons of MM household and learning household. As such, in theillustrated example of FIG. 12, the most similar household match isbetween the Smith household (one of the MM households 106) and the Leehousehold (one of the learning households 104) because it has the lowestrelative MLV score value of 0.11.

In some examples, when making a comparison between persons of the MMhouseholds and persons of the one or more learning households toidentify a closest match based on the MLV score, a greater priority maybe assigned to whether such matching learning household(s) are alsowithin the same designated market area (DMA) as the MM household. Theexample rank engine 248 may identify, for each comparison between acandidate MM household of interest and one or more learninghousehold(s), whether a corresponding learning household is also withinthe same DMA as the MM household, which is shown in the example DMAcolumn 1220. In the event a matching DMA status is to receive a greaterpriority than the MLV score, then the example MLV engine 240 willidentify a closest match between the Smith household and the Joneshousehold, despite the fact that the MLV score therebetween is 0.13,which is relatively greater than the MLV score between the Smithhousehold and the Lee household (i.e., an MLV score of 0.11). In someexamples, if a matching learning household is within a same DMA, but thecorresponding MLV score is not a lowest relative value, then the in-DMAhousehold is used only if it is within a threshold value of the overalllowest MLV score. In still other examples, if none of the households iswithin the DMA of interest (and within a threshold of the overall lowestMLV score), then the home with the lowest MLV score is used. While theillustrated example of FIG. 12 includes comparisons between a single MMhousehold (i.e., the Smith household) and three (3) candidate learninghouseholds, examples disclosed herein are not limited thereto. Inparticular, the calculation of MLV scores and comparisons may occurbetween additional and/or alternate MM households and correspondingcandidate learning households. At this point of the third phase, personswithin the MM households are matched to the closest candidate learninghouseholds (and persons therein) based on the MLV score and/or the MLVscore in view of a DMA priority. Next, the one or more individualswithin the MM households and corresponding matching learning householdsare evaluated so that viewing behavior(s) of the learning householdmember(s) can be imputed to the most appropriate MM household member(s).In the illustrated example of FIG. 13, an MM home located in San Diego1302 is found to best match a learning household within that same DMA1304. Both households include three household members and correspondingprobability values. As described above, each household member includes acorresponding MLV rank determined by the example rank engine 248, andtentative associations between those household members with the same MLVrank are deemed to match 1306.

However, because viewing amounts between matched MM households andlearning households may differ, the example quarter hours therebetweenare misaligned. For example, while a similarity match was identifiedbetween a candidate MM household and a candidate learning household, anumber of quarter hour data points collected in the example MM householdmay differ from the number of quarter hour data points collected in thecorresponding matching learning household. For instance, between adaypart of 7:00-11:00 PM the example MM households may have collectedseven (7) quarter hours of tuning data, while a corresponding learninghouseholds may have only collected four (4) quarter hours of viewingdata, thereby creating a discrepancy that has traditionally resulted inerroneous imputation predictions and wasteful discarding ofnon-overlapping data points. Examples disclosed herein reduce animputation error and preserve data points during one or more imputationefforts between MM households and learning households.

FIG. 14 illustrates a portion of an example alignment table 1400 thatincludes an example quarter hour column 1402, an example person IDcolumn 1404, an example MLV rank column 1406, an example potentialviewing minutes column 1408, an example initial quarter hour ordercolumn 1410, an example adjusted quarter hour column 1412, an examplefinal quarter hour column 1414, an example learning household viewingstatus column 1416, and an example imputed viewed minutes column 1418.

In the illustrated example of FIG. 14, the quarter hour column 1402includes quarter hour values associated with available quarter hourpotential viewing minutes from a MM household of interest. In theillustrated example of FIG. 14, the person associated with “Person ID 2”has a quantity of seven (7) quarter hour data points 1420. The exampleminutes aggregator 244 assigns each quarter hour data point 1420 in atemporal order, as shown in the example initial QH order column 1410.Stated differently, the example temporal order values are sequentialinteger placeholders of each available quarter hour data point from theexample MM household. However, the matched learning household onlyincludes a quantity of four quarter hour data points, therebyillustrating a lack of parity between these two households of interest.Rather than drop, delete and/or otherwise simply eliminate quarter hourdata points that do not have a corresponding parity match, the exampleminutes aggregator 244 reduces imputation errors and preserves utilityof all available data points by calculating an adjusted quarter hourratio based on the difference between available MM household quarterhour data points and available learning household quarter hour datapoints in a manner consistent with example Equation (9).

$\begin{matrix}{{{Adjusted}\mspace{14mu}{QH}} = {\frac{{Available}\mspace{14mu}{Learning}\mspace{14mu}{HH}\mspace{14mu}{QHs}}{{Available}\mspace{14mu}{MM}\mspace{14mu}{HH}\mspace{14mu}{QHs}}.}} & {{Equation}\mspace{14mu}(9)}\end{matrix}$Continuing with the example above, an adjusted QH value of 0.571 resultswhen the learning household includes four (4) available quarter hourdata points and the MM households include seven (7) available quarterhour data points. The example minutes aggregator 244 multiplies theadjusted QH ratio by the initial QH order (column 1410) to derive anadjusted QH order (column 1412), which is rounded to result in a finalQH order (column 1414). As such, respective ones of the relatively fewernumber of learning household data points are expanded to overlap withthe relatively greater number of MM household data points. In the eventthat the matched person from the learning household was viewing during aparticular quarter hour, the example MLV engine 240 designates thepotential viewing minutes from the MM household as actual viewingminutes, as shown in column 1418. In other words, potential viewingminutes (tuning minutes) associated with the tuning household areimputed and/or otherwise deemed to be viewing minutes when a matchingquarter hour in the viewing household exhibits viewing behavior at thesame time. While examples above refer to data points associated with aquarter hour time period resolution, such examples are disclosed forillustration and not limitation. While examples disclosed above considerviewing and tuning behaviors from members of respective households, insome examples, short-term visitor viewing is collected from the learninghousehold. As such, examples disclosed above also apply to such visitorviewing, which is carried over to MM households for a similar analysis.

To illustrate, FIG. 15 includes the MM household located in San Diego1502 and the previously identified matching learning household 1504, asdescribed above in connection with FIG. 13. From the learning household1504, because the example household member Mike 1506 was viewing duringthe same quarter hour as the example household member Jim 1508 from theMM household 1502, Jim is imputed as a viewer during that quarter hourand the corresponding potential viewing minutes are deemed to be actualviewing minutes for Jim. On the other hand, because the examplehousehold member Steven 1510 from the learning household 1504 was notviewing at the same quarter hour as the matched household member Richard1512 in the MM household 1502, then Richard is not deemed to be a viewerand any associated potential viewing minutes are not attributed toRichard in the MM household 1502.

While an example manner of implementing the viewer assignment engine 110of FIGS. 1 and 2 is illustrated in FIG. 2, one or more of the elements,processes and/or devices illustrated in FIG. 2 may be combined, divided,re-arranged, omitted, eliminated and/or implemented in any other way.Further, the example classification engine 200, the example probabilityengine 220, the example most likely viewer (MLV) engine 240, the examplelearning household interface 202, the example media meter interface 204,the example weighting engine 206, the example cell generator 208, theexample stage selector 210, the example independent distributionprobability (IDP) selector 212, the example total probability calculator222, the example marginal probability calculator 224, the example oddsratio calculator 226, the example odds appending engine 228, the examplecell selector 242, the example minutes aggregator 244, the exampleaverage probability calculator 246, the example rank engine 248 and/or,more generally, the example viewer assignment engine 110 of FIGS. 1 and2 may be implemented by hardware, software, firmware and/or anycombination of hardware, software and/or firmware. Thus, for example,any of the example classification engine 200, the example probabilityengine 220, the example MLV engine 240, the example learning householdinterface 202, the example media meter interface 204, the exampleweighting engine 206, the example cell generator 208, the example stageselector 210, the example IDP selector 212, the example totalprobability calculator 222, the example marginal probability calculator224, the example odds ratio calculator 226, the example odds appendingengine 228, the example cell selector 242, the example minutesaggregator 244, the example average probability calculator 246, theexample rank engine 248 and/or, more generally, the example viewerassignment engine 110 of FIGS. 1 and 2 could be implemented by one ormore analog or digital circuit(s), logic circuits, programmableprocessor(s), application specific integrated circuit(s) (ASIC(s)),programmable logic device(s) (PLD(s)) and/or field programmable logicdevice(s) (FPLD(s)). When reading any of the apparatus or system claimsof this patent to cover a purely software and/or firmwareimplementation, at least one of the example classification engine 200,the example probability engine 220, the example MLV engine 240, theexample learning household interface 202, the example media meterinterface 204, the example weighting engine 206, the example cellgenerator 208, the example stage selector 210, the example IDP selector212, the example total probability calculator 222, the example marginalprobability calculator 224, the example odds ratio calculator 226, theexample odds appending engine 228, the example cell selector 242, theexample minutes aggregator 244, the example average probabilitycalculator 246, the example rank engine 248 and/or, more generally, theexample viewer assignment engine 110 of FIGS. 1 and 2 is/are herebyexpressly defined to include a tangible computer readable storage deviceor storage disk such as a memory, a digital versatile disk (DVD), acompact disk (CD), a Blu-ray disk, etc. storing the software and/orfirmware. Further still, the example viewer assignment engine of FIGS. 1and 2 may include one or more elements, processes and/or devices inaddition to, or instead of, those illustrated in FIG. 2, and/or mayinclude more than one of any or all of the illustrated elements,processes and devices.

Flowcharts representative of example machine readable instructions forimplementing the viewer assignment engine 110 of FIGS. 1 and 2 are shownin FIGS. 16-24. In these examples, the machine readable instructionscomprise a program for execution by a processor such as the processor2412 shown in the example processor platform 2400 discussed below inconnection with FIG. 24. The program(s) may be embodied in softwarestored on a tangible computer readable storage medium such as a CD-ROM,a floppy disk, a hard drive, a digital versatile disk (DVD), a Blu-raydisk, or a memory associated with the processor 2412, but the entireprogram(s) and/or parts thereof could alternatively be executed by adevice other than the processor 2412 and/or embodied in firmware ordedicated hardware. Further, although the example program(s) is/aredescribed with reference to the flowcharts illustrated in FIGS. 16-24,many other methods of implementing the example viewer assignment engine110 may alternatively be used. For example, the order of execution ofthe blocks may be changed, and/or some of the blocks described may bechanged, eliminated, or combined.

As mentioned above, the example processes of FIGS. 16-24 may beimplemented using coded instructions (e.g., computer and/or machinereadable instructions) stored on a tangible computer readable storagemedium such as a hard disk drive, a flash memory, a read-only memory(ROM), a compact disk (CD), a digital versatile disk (DVD), a cache, arandom-access memory (RAM) and/or any other storage device or storagedisk in which information is stored for any duration (e.g., for extendedtime periods, permanently, for brief instances, for temporarilybuffering, and/or for caching of the information). As used herein, theterm tangible computer readable storage medium is expressly defined toinclude any type of computer readable storage device and/or storage diskand to exclude propagating signals and to exclude transmission media. Asused herein, “tangible computer readable storage medium” and “tangiblemachine readable storage medium” are used interchangeably. Additionallyor alternatively, the example processes of FIGS. 16-24 may beimplemented using coded instructions (e.g., computer and/or machinereadable instructions) stored on a non-transitory computer and/ormachine readable medium such as a hard disk drive, a flash memory, aread-only memory, a compact disk, a digital versatile disk, a cache, arandom-access memory and/or any other storage device or storage disk inwhich information is stored for any duration (e.g., for extended timeperiods, permanently, for brief instances, for temporarily buffering,and/or for caching of the information). As used herein, the termnon-transitory computer readable medium is expressly defined to includeany type of computer readable storage device and/or storage disk and toexclude propagating signals and to exclude transmission media. As usedherein, when the phrase “at least” is used as the transition term in apreamble of a claim, it is open-ended in the same manner as the term“comprising” is open ended.

The program 1600 of FIG. 16 includes an example first phase thatclassifies data from learning households 104 and MM households 106 toidentify model dimensions contained therein (1602). The example program1600 of FIG. 16 also includes an example second phase that calculatesprobabilities for the example learning households 104 in view of IDPvalues (1604), and includes an example third phase in which the exampleMLV engine 240 uses the calculated probabilities to identify and/orassign viewing behaviors for household members of the example MMhouseholds 106 (1606). The example program 1600 of FIG. 16 begins atblock 1608 in which the learning household interface 202 and the MMinterface 204 acquire panelist exposure data (sometimes referred toherein as “exposure minutes” or “people meter data”), and panelist mediameter (MM) data (sometimes referred to herein as “tuning minutes”) for ageography of interest.

As described above, because collected data that is more recent exhibitsan accuracy that is better than that of relatively older data, theexample weighting engine 206 applies importance weighting values to thedata collected from the example learning households 104 and the exampleMM households 106 (block 1610). In view of the example three phases ofFIG. 16, at least one goal includes identifying which learninghouseholds best match MM households based on different criteria, such assimilarity of household composition, similarity of householdcharacteristics (dimensions), and/or similarity of household probabilityvalues. The example cell generator 208 generates, receives and/orotherwise retrieves different stages of intersecting dimensions ofinterest (block 1612). As described above in connection with FIG. 5, afirst stage of dimensions may delineate a particular quantity ofdimensions that, if included within a household, defines a householdclassification when comparing it to one or more other households. In theevent a particular household does not satisfy the quantity of dimensionsdelineated in the example first stage, then the example classificationengine 200 evaluates that household in view of another (subsequent,e.g., Stage 2) stage that is relatively less restrictive. The exampleclassification engine 200 classifies exposure and tuning data within thehouseholds to be used for later comparisons (block 1614). As describedabove, and as described in further detail below, the exampleclassification engine 200 considers both intersecting dimensions for thehousehold and members therein, as well as IDP dimensions. Once aparticular household and/or members included therein has/have beenclassified, any future comparisons of the data from those householdswill be limited to only other households that share the sameclassification (e.g., Stage 1 households, Stage 2 households, etc.).

In the illustrated example of FIG. 16, the example second phase 1604 ofthe example program 1600 begins at block 1616, in which the exampleprobability engine 220 calculates probability values of the learninghousehold data for each demographic dimension of interest to derive oddsratios. As described above, and in further detail below, the odds ratiosconsider both intersecting dimensions of households as well as IDPdimensions, which allow final probability values to be adjusted in amanner that accounts for as many predictive dimensions of imputedviewing behaviors in the MM households as possible, given adequatesample sizes. Additionally, the odds ratios consider both intersectingdimensions of households as well as IDP dimensions to allow finalprobability values to be adjusted in a manner that reducesoverrepresentation or underrepresentation of imputed viewing behaviorsin the MM households. The example odds appending engine 228 assigns thefinal probability values to household members of both the examplelearning households 104 and the example MM households 106 (block 1618).

In the illustrated example of FIG. 16, the example third phase 1606 ofthe example program 1600 begins at block 1620, in which the example MLVengine 240 imputes viewers within each MM household 106 using a matcheddonor-based approach. In this approach, each TV set in each MM household106 is matched with a characteristically and behaviorally similarlearning household/TV set. Individuals within the matched homes are thenmatched on a person-by-person basis based on their probability rankingwithin the home (e.g., most likely to least likely). Then, after quarterhours or viewing/potential viewing minutes are aligned between thehomes, potential viewers from the MM home are imputed as actual viewersif the corresponding MLV-ranked individual in the matched learning homeviewed during that time.

Returning to the example first phase 1602 of FIG. 16, additional detailin connection with classifying the exposure and tuning data of block1614 is shown in FIG. 17. In the illustrated example of FIG. 17, theexample learning household interface 202 or the example MM interface 204selects one or more households of interest (block 1702). For instance,the example program 1614 of FIG. 17 may iterate any number of times whenprocessing data from MM households, in which the example MM interface204 is invoked. However, when the example program 1614 of FIG. 17iterates any number of times when processing data from learninghouseholds, then the example learning household interface 202 is invokedby the example classification engine 200. The example cell generator 208selects a target set of demographic dimension(s) of interest within ageography of interest (block 1704), as described above in connectionwith FIG. 7. Additionally, the example stage selector 210 selects acandidate stage of intersecting dimensions (block 1706) (e.g., Stage 1),and evaluates whether the target set of demographic dimension(s)satisfies a required number of available households (block 1708). Ifnot, then the example stage selector 210 reverts to a subsequent stagethat is defined with relatively fewer intersecting dimensions (block1710), thereby improving the chances of a greater number of qualifyinghouseholds. Control returns to block 1708 to determine whether thesubsequent stage satisfies a required number of available householdsand, if so, the example classification engine 200 classifies thedemographic of interest as associated with only those households thatsatisfy the qualifying stage (block 1712).

The example cell generator 208 determines whether one or more additionaldemographic dimensions of interest are to be evaluated (block 1714). Forinstance, in the illustrated example of FIG. 7, the demographicdimensions of interest included one male and one female age 35-54, andone child age 2-11, all of which are in the Pacific territory. In theevent an alternate combination of demographic dimensions of interest areto be evaluated, control returns to block 1704. Additionally, in theevent an additional or alternate household type of interest is to beassessed (e.g., MM households or learning households) (block 1716), thencontrol returns to block 1702. Otherwise, the example IDP selector 212classifies the IDP dimensions in view of the demographic dimensions ofinterest (block 1718), as described below in connection with FIG. 18.

As described above, a household and/or members therein can only beassociated with a particular stage of cell dimensions when suchdimensions intersect (e.g., each dimension is true as a logical “AND”condition). However, examples disclosed herein also evaluate householdsand members therein in view of IDP dimensions in a manner that isindependent of one or more other IDP dimensions. In the illustratedexample of FIG. 18, the classification engine 200 selects a previouslyclassified demographic dimension of interest (block 1802), and the IDPselector 212 selects a candidate IDP dimension from the same stage thatis associated with that previously classified dimension of interest(block 1804). The example IDP selector 212 determines whether theselected IDP dimension of interest has an associated threshold number ofavailable households (data points) (block 1806) and, if not, that IDPdimension is ignored from further evaluation (block 1808). On the otherhand, in the event the selected IDP dimension of interest includes anassociated threshold number of available households (block 1806), thenthat IDP dimension is used and/or otherwise retained for future use whencalculating probabilities (block 1810).

The example classification engine 200 determines whether the selectedstage includes one or more additional IDP dimensions (block 1812) and,if so, control returns to block 1804. If not, the example classificationengine 200 determines whether one or more additional previouslyclassified demographic dimensions of interest are to be evaluated (block1814) and, if so, control returns to block 1802.

Returning to the example second phase 1604 of FIG. 16, additional detailin connection with calculating probabilities of learning household dataof block 1616 is shown in FIG. 19. Generally speaking, the exampleprogram 1616 of FIG. 19 calculates (a) total probabilities and (b) totalodds for intersecting cell dimensions associated with data from learninghouseholds, and calculates (c) marginal probabilities and (d) marginalodds for IDP dimensions associated with data from those learninghouseholds. In the illustrated example of FIG. 19, the classificationengine 200 selects a demographic of interest, such as a male age 35-54(block 1902). The example learning household interface 202 retrievescorresponding exposure minutes of the demographic of interest fromhouseholds that match the previously determined classified stage ofintersecting dimensions (block 1904). As described above, while onedemographic of interest is selected, each household may have one or moreadditional household members that may contribute to tuning behaviors. Assuch, the example learning household interface 202 retrievescorresponding exposure minutes from all other household members aspotential viewing minutes (block 1906). As described above in connectionwith Equation (2), the example total probability calculator 222calculates a total probability as a ratio of the sum of exposure minutesfor the demographic of interest (e.g., the male age 35-54) and the sumof potential viewing minutes from other household members (e.g., thefemale age 35-54 and the child age 2-11) (block 1908). Additionally, theexample total probability calculator 222 calculates a total odds valuein a manner consistent with Equation (3) (block 1910).

In the event one or more additional demographics of interest is to beconsidered (block 1912), then control returns to block 1902. Otherwise,the example learning household interface 202 retrieves exposure minutesof the demographic of interest from households that match the previouslydetermined classified stage of IDP dimensions (block 1914), as well asretrieving exposure minutes from all other household members aspotential viewing minutes (block 1916). The example marginal probabilitycalculator 224 calculates a marginal probability as a ratio of the sumof exposure minutes for the demographic of interest and the sum ofpotential exposure minutes for all other household members (block 1918),as shown above in Equation (4). Additionally, the example marginalprobability calculator 224 calculates a marginal odds value in a mannerconsistent with Equation (5) (block 1920).

In the event the example classification engine 200 identifies one ormore additional IDP dimensions of interest are associated with thedemographic of interest (block 1922), then control returns to block1914. If not, then the example classification engine 200 determineswhether another demographic of interest is to be evaluated with the IDPdimensions (block 1924). If so, then another demographic of interest isselected (block 1926) and control returns to block 1914. Now that (a)all marginal odds values for each demographic of interest andcorresponding IDP dimension are calculated and (b) all total odds valuesfor each demographic of interest are calculated, the example odds ratiocalculator 226 calculates an odds ratio in a manner consistent withexample Equation (6) (block 1928).

Returning to the example second phase 1604 of FIG. 16, additional detailin connection with assigning updated probabilities of block 1618 isshown in FIG. 20. In the illustrated example of FIG. 20, the exampleclassification engine 200 identifies dimension matches between theclassified learning households 104 and corresponding MM households 106that share the same stage classification (block 2002). For eachdemographic of interest, the example odds appending engine 228calculates an adjusted odds value in a manner consistent with Equation(7) (block 2004), and converts the adjusted odds values to a finalprobability values in a manner consistent with Equation (8) (block2006). Now that the final probability values are available for eachdemographic of interest, the second stage ends and those finalprobability values are used to identify most likely viewers in stage 3.

Returning to the example third phase 1606 of FIG. 16, additional detailin connection with identifying the MLV for each member in MM householdsof block 1620 is shown in FIG. 21. Generally speaking, the third phaseevaluates each available MM household to identify a correspondingmatching learning household so that behaviors from that matchinglearning household may be imputed to the MM household. In theillustrated example of FIG. 21, the example MM interface 204 selects acandidate MM household (block 2102), and the example classificationengine identifies a corresponding classification associated with that MMhousehold (block 2104) using a subset of the cell-level classificationdimensions from the first phase. For example, the selected MM householdmay be classified as using stage 1 cell dimensions. These stagecharacteristics are used later when selecting candidate learninghouseholds that may be appropriate matches with the selected MMhousehold.

The example average probability calculator 246 calculates an averageprobability value for each cell combination and corresponding person(household member) (block 2116), averaging the probabilities across eachperson's potential viewing (MM data) or viewing (learning data) inwithin the given cell, described above in connection with FIG. 11. Basedon the values of the average probabilities for each household member,the example rank engine 248 applies a rank value for each householdmember from highest to lowest probability (block 2118). In the event theexample MM interface 204 determines that one or more additional MMhouseholds of interest remain that have not yet calculated averageprobability values for their respective household members (block 1220),then control returns to block 2102 to select another candidate MMhousehold. Otherwise, the example MLV engine 240 matches the MMhouseholds to corresponding learning households (block 2122), matchesthe household members between the MM households and correspondinglearning households (block 2124), and imputes viewing behaviors from thelearning households to the MM households (block 2126), as described infurther detail below.

FIG. 22 illustrates additional detail related to matching the MMhouseholds to corresponding learning households of block 2122. In theillustrated example of FIG. 22, the example MM interface 204 selects acandidate MM household to match with any number of candidate learninghouseholds that have already been narrowed down based on theircorresponding classifications from the first phase (block 2202). Theexample learning household interface 202 selects one of the candidatelearning households that could be a potential match for the selected MMhousehold (block 2204), and the example MLV engine 240 selects the datafrom the candidate learning household and then compares individualsbetween the matched households having the same MLV rank value (block2206). The example MLV rank engine 240 calculates an absolute differencebetween the average probability values of each individual within the MMhousehold and corresponding candidate learning household (block 2208),which can be added to the example MLV matching table 1200 as describedabove in connection with FIG. 12. If the example learning householdinterface 202 determines that additional candidate learning householdsare available for consideration (block 2210), then control returns toblock 2204.

The example rank engine 248 calculates MLV scores for each paired MMhousehold and learning household based on the sum of the individuals'absolute difference values therebetween (block 2212). The example rankengine 248 selects a final match of the MM household and best candidatelearning household based on the lowest relative MLV score (block 2216).If the best candidate learning household is in the same DMA, or there isan in-DMA learning household with an MLV score within a particular rangeof the lowest MLV score, then priority is given to the in-DMA home andit is used as the best match. On the other hand, in the event a learninghome is in the same DMA as the MM household within the lowest MLV scoreor a threshold range (e.g., a range deemed acceptable) of the lowest MLVscore is not available, then the household with the lowest MLV score issimply used even if it is not in the same DMA as the MM home (block2218). In either case, the closest households are now matched, and thepersons therebetween are also matched based on similar MLV rank values(see columns 1204 and 1212 of FIG. 12).

Now that the best match between MM households and corresponding learninghouseholds has been determined, and members within those households havebeen matched, additional detail related to imputing viewing behaviorwithin those matched households is described in further detail in FIG.23. In other words, while persons within a learning household arematched to persons in the MM household, potential viewing minutes arenot automatically deemed actual viewing minutes. Instead, as shown inthe illustrated example of FIG. 23, the example rank engine 248 selectsa matched MM household and corresponding learning household (block2302), and temporally orders the collected quarter hour data by person(block 2304), as shown in connection with FIG. 14. The example minutesaggregator 244 calculates an adjusted QH ratio that is based ondifferences between available QH data points in an MM household versusthe matched learning household (block 2306). As described above,differences in available QH data points between matched households mayoccur when one household includes a greater or lesser number of QH datapoints than another household during the comparison, thereby resultingin a lack of parity. In the example described in connection with FIG.14, the example learning household included, during the same daypart,four (4) quarter hour data points, while the example MM householdincluded seven (7) quarter hour data points. As such, the exampleminutes aggregator 244 calculated the adjusted QH ratio as 0.571.

The example minutes aggregator 244 multiplies the QH ratio by eachordered quarter hour data point value to derive an adjusted QH order(block 2308), and rounds the result to derive a final QH order value(block 2310). To reduce imputation errors that may typically occur whenmerely discarding data points that do not have exact parity, during asimilar quarter hour of interest the example MLV engine 240 determineswhether a particular household member within the learning home exhibitsviewing behavior and, if so, potential viewing minutes from thecorresponding MM household are imputed as actual viewing (block 2312).Further, any short-term visitor viewing from the learning household/TVset is carried over to the MM household/TV set (note that long-termvisitors are considered the same way as regular household members).

FIG. 24 is a block diagram of an example processor platform 2400 capableof executing the instructions of FIGS. 16-24 to implement the apparatusof FIGS. 1 and 2. The processor platform 2400 can be, for example, aserver, a personal computer, an Internet appliance, a set top box, orany other type of computing device.

The processor platform 2400 of the illustrated example includes aprocessor 2412. The processor 2412 of the illustrated example ishardware. For example, the processor 2412 can be implemented by one ormore integrated circuits, logic circuits, microprocessors or controllersfrom any desired family or manufacturer.

The processor 2412 of the illustrated example includes a local memory2413 (e.g., a cache) and the viewer assignment engine 110. The processor2412 of the illustrated example is in communication with a main memoryincluding a volatile memory 2414 and a non-volatile memory 2416 via abus 2418. The volatile memory 2414 may be implemented by SynchronousDynamic Random Access Memory (SDRAM), Dynamic Random Access Memory(DRAM), RAMBUS Dynamic Random Access Memory (RDRAM) and/or any othertype of random access memory device. The non-volatile memory 2416 may beimplemented by flash memory and/or any other desired type of memorydevice. Access to the main memory 2414, 2416 is controlled by a memorycontroller.

The processor platform 2400 of the illustrated example also includes aninterface circuit 2420. The interface circuit 2420 may be implemented byany type of interface standard, such as an Ethernet interface, auniversal serial bus (USB), and/or a PCI express interface.

In the illustrated example, one or more input devices 2422 are connectedto the interface circuit 2420. The input device(s) 2422 permit(s) a userto enter data and commands into the processor 1012. The input device(s)can be implemented by, for example, an audio sensor, a microphone, akeyboard, a button, a mouse, a touchscreen, a track-pad, a trackball,isopoint and/or a voice recognition system.

One or more output devices 2424 are also connected to the interfacecircuit 2420 of the illustrated example. The output devices 2424 can beimplemented, for example, by display devices (e.g., a light emittingdiode (LED), an organic light emitting diode (OLED), a liquid crystaldisplay, a cathode ray tube display (CRT), a touchscreen and/orspeakers). The interface circuit 2420 of the illustrated example, thus,typically includes a graphics driver card, a graphics driver chip or agraphics driver processor.

The interface circuit 2420 of the illustrated example also includes acommunication device such as a transmitter, a receiver, a transceiver, amodem and/or network interface card to facilitate exchange of data withexternal machines (e.g., computing devices of any kind) via a network2426 (e.g., an Ethernet connection, a digital subscriber line (DSL), atelephone line, coaxial cable, a cellular telephone system, etc.).

The processor platform 2400 of the illustrated example also includes oneor more mass storage devices 2428 for storing software and/or data.Examples of such mass storage devices 2428 include floppy disk drives,hard drive disks, compact disk drives, Blu-ray disk drives, RAIDsystems, and digital versatile disk (DVD) drives.

The coded instructions 2432 of FIGS. 16-23 may be stored in the massstorage device 2428, in the volatile memory 2414, in the non-volatilememory 2416, and/or on a removable tangible computer readable storagemedium such as a CD or DVD.

From the foregoing, it will be appreciated that the above disclosedmethods, apparatus and articles of manufacture permit the identificationof unique panelist member media viewing behavior in households that donot include a People Meter. Additionally, examples disclosed hereinreduce costs related to personnel and equipment by facilitating a mannerof viewing behavior identification with lower cost media meteringdevices that reduce and/or eliminate a need for professional and/oron-site personnel installation and/or maintenance. Further, examplesdisclosed herein improve a statistical reliability of imputation via theapplication of independent distribution probability dimensions, whichimprove data granularity and predictive confidence. Additional examplesdisclosed herein reduce waste of and/or otherwise discarding data pointsbetween compared households by aligning dissimilar temporal data pointswhen such households do not exhibit time period parity of such datapoints.

Although certain example methods, apparatus and articles of manufacturehave been disclosed herein, the scope of coverage of this patent is notlimited thereto. On the contrary, this patent covers all methods,apparatus and articles of manufacture fairly falling within the scope ofthe claims of this patent.

What is claimed is:
 1. An apparatus to impute panelist household mediabehavior, the apparatus comprising: memory to store computer readableinstructions; and a processor to execute the computer readableinstructions to at least: determine first probabilities for firstpanelists in a media meter household during a first set of time periods,the media meter household including a first meter of a first type tomonitor a first media presentation device, the first probabilities to becalculated based on first panelist behavior data collected by the firstmeter of the first type, the first panelist behavior data to identify afirst number of minutes of first media played by the first mediapresentation device without uniquely identifying ones of the firstpanelists exposed to the first media; determine second probabilities forsecond panelists in a plurality of learning households during a secondset of time periods, the plurality of learning households includingcorresponding second meters to monitor corresponding second mediapresentation devices, the second meters of a second type different fromthe first type, the second probabilities to be calculated based onsecond panelist behavior data collected by the second meters of thesecond type, the second panelist behavior data to (i) identify a secondnumber of minutes of second media played by the second mediapresentation devices associated with respective ones of the secondmeters of the second type and (ii) uniquely identify individual ones ofthe second panelists exposed to the respective second media; compare thefirst probabilities and the second probabilities to identify a candidatehousehold from the plurality of learning households to associate withthe media meter household; and impute ones of the first number ofminutes to individual ones of the first panelists when the secondpanelist behavior data associated with the candidate household indicatesactivity during one of the second set of time periods that matchesactivity in the media meter household during one of the first set oftime periods.
 2. The apparatus as defined in claim 1, wherein theprocessor is to identify a pool of potential candidate households fromamong the plurality of learning households based on comparison ofrespective household characteristics of the plurality of learninghouseholds with household characteristics of the media meter household.3. The apparatus as defined in claim 2, wherein the processor is toidentify the candidate household from among the pool of potentialcandidate households based on an absolute difference value between anaverage value of the first probabilities and respective ones of averagevalues of the second probabilities associated with the pool of potentialcandidate households.
 4. The apparatus as defined in claim 2, whereinthe processor is to: determine a size of the pool of the potentialcandidate households based on a set of specified householdcharacteristics; and in response to the size of the pool not satisfyinga threshold size, decrease a number of specified householdcharacteristics included in the set of specified householdcharacteristics.
 5. The apparatus as defined in claim 1, wherein theprocessor is to determine an adjusted quarter hour ratio when a numberof the first set of time periods is dissimilar to a number of the secondset of time periods, the adjusted quarter hour ratio corresponding tothe number of the second set of time periods divided by the number ofthe first set of time periods.
 6. The apparatus as defined in claim 5,wherein the processor is to multiply the adjusted quarter hour ratio bya temporal placeholder associated with the first set of time periods togenerate a final quarter hour order value associated with the second setof time periods.
 7. The apparatus as defined in claim 6, wherein theprocessor is to expand the number of the second set of time periods tomatch the first set of time periods based on the final quarter hourorder value.
 8. A non-transitory computer readable medium comprisinginstructions that, when executed, cause a processor to at least:determine first probabilities for first panelists in a media meterhousehold during a first set of time periods, the media meter householdincluding a first meter of a first type to monitor a first mediapresentation device, the first probabilities to be calculated based onfirst panelist behavior data collected by the first meter of the firsttype, the first panelist behavior data to identify a first number ofminutes of first media played by the first media presentation devicewithout uniquely identifying ones of the first panelists exposed to thefirst media; determine second probabilities for second panelists in aplurality of learning households during a second set of time periods,the plurality of learning households including corresponding secondmeters to monitor corresponding second media presentation devices, thesecond meters of a second type different from the first type, the secondprobabilities to be calculated based on second panelist behavior datacollected by the second meters of the second type, the second panelistbehavior data to (i) identify a second number of minutes of second mediaplayed by the second media presentation devices associated withrespective ones of the second meters of the second type and (ii)uniquely identify individual ones of the second panelists exposed to therespective second media; compare the first probabilities and the secondprobabilities to identify a candidate household from the plurality oflearning households to associate with the media meter household; andimpute ones of the first number of minutes to individual ones of thefirst panelists when the second panelist behavior data associated withthe candidate household indicates activity during one of the second setof time periods that matches activity in the media meter householdduring one of the first set of time periods.
 9. The non-transitorycomputer readable medium as defined in claim 8, wherein the instructionsfurther cause the processor to identify a pool of potential candidatehouseholds from among the plurality of learning households based oncomparison of respective household characteristics of the plurality oflearning households with household characteristics of the media meterhousehold.
 10. The non-transitory computer readable medium as defined inclaim 9, wherein the instructions further cause the processor toidentify the candidate household from among the pool of potentialcandidate households based on an absolute difference value between anaverage value of the first probabilities and respective ones of averagevalues of the second probabilities associated with the pool of potentialcandidate households.
 11. The non-transitory computer readable medium asdefined in claim 9, wherein the instructions further cause the processorto: determine a size of the pool of the potential candidate householdsbased on a set of specified household characteristics; and in responseto the size of the pool not satisfying a threshold size, decrease anumber of specified household characteristics included in the set ofspecified household characteristics.
 12. The non-transitory computerreadable medium as defined in claim 8, wherein the instructions furthercause the processor to determine an adjusted quarter hour ratio when anumber of the first set of time periods is dissimilar to a number of thesecond set of time periods, the adjusted quarter hour ratiocorresponding to the number of the second set of time periods divided bythe number of the first set of time periods.
 13. The non-transitorycomputer readable medium as defined in claim 12, wherein theinstructions further cause the processor to multiply the adjustedquarter hour ratio by a temporal placeholder associated with the firstset of time periods to generate a final quarter hour order valueassociated with the second set of time periods.
 14. The non-transitorycomputer readable medium as defined in claim 13, wherein theinstructions further cause the processor to expand the number of thesecond set of time periods to match the first set of time periods basedon the final quarter hour order value.
 15. A method to impute panelisthousehold media behavior, the method comprising: determining, byexecuting an instruction with a processor, first probabilities for firstpanelists in a media meter household during a first set of time periods,the media meter household including a first meter of a first type tomonitor a first media presentation device, the first probabilitiescalculated based on first panelist behavior data collected by the firstmeter of the first type, the first panelist behavior data to identify afirst number of minutes of first media played by the first mediapresentation device without uniquely identifying ones of the firstpanelists exposed to the first media; determining, by executing aninstruction with the processor, second probabilities for secondpanelists in a plurality of learning households during a second set oftime periods, the plurality of learning households includingcorresponding second meters to monitor corresponding second mediapresentation devices, the second meters of a second type different fromthe first type, the second probabilities calculated based on secondpanelist behavior data collected by the second meters of the secondtype, the second panelist behavior data to (i) identify a second numberof minutes of second media played by the second media presentationdevices associated with respective ones of the second meters of thesecond type and (ii) uniquely identify individual ones of the secondpanelists exposed to the respective second media; comparing, byexecuting an instruction with the processor, the first probabilities andthe second probabilities to identify a candidate household from theplurality of learning households to associate with the media meterhousehold; and imputing, by executing an instruction with the processor,ones of the first number of minutes to individual ones of the firstpanelists when the second panelist behavior data associated with thecandidate household indicates activity during one of the second set oftime periods that matches activity in the media meter household duringone of the first set of time periods.
 16. The method as defined in claim15, further including identifying a pool of potential candidatehouseholds from among the plurality of learning households based oncomparison of respective household characteristics of the plurality oflearning households with household characteristics of the media meterhousehold.
 17. The method as defined in claim 16, wherein theidentifying of the candidate household from among the pool of potentialcandidate households is based on an absolute difference value between anaverage value of the first probabilities and respective ones of averagevalues of the second probabilities associated with the pool of potentialcandidate households.
 18. The method as defined in claim 16, furtherincluding: determining a size of the pool of the potential candidatehouseholds based on a set of specified household characteristics; and inresponse to the size of the pool not satisfying a threshold size,decreasing a number of specified household characteristics included inthe set of specified household characteristics.
 19. The method asdefined in claim 15, further including determining an adjusted quarterhour ratio when a number of the first set of time periods is dissimilarto a number of the second set of time periods, the adjusted quarter hourratio corresponding to the number of the second set of time periodsdivided by the number of the first set of time periods.
 20. The methodas defined in claim 19, further including multiplying the adjustedquarter hour ratio by a temporal placeholder associated with the firstset of time periods to generate a final quarter hour order valueassociated with the second set of time periods.